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2012
Computer-Based Cognitive Training for AgeRelated Cognitive Decline and Mild Cognitive
Impairment
James Fortman
Antioch University - Santa Barbara
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Fortman, James, "Computer-Based Cognitive Training for Age-Related Cognitive Decline and Mild Cognitive Impairment" (2012).
Dissertations & Theses. 97.
http://aura.antioch.edu/etds/97
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COMPUTER-BASED COGNITIVE TRAINING FOR AGE-RELATED
COGNITIVE DECLINE AND MILD COGNITIVE IMPAIRMENT
A dissertation submitted
by
JAMES FORTMAN
To
ANTIOCH UNIVERSITY SANTA BARBARA
in partial fulfillment of
the requirements for the
degree of
DOCTOR OF PSYCHOLOGY
In
CLINICAL PSYCHOLOGY
__________________________________________________
JULIET ROHDE-BROWN, Ph.D.
Dissertation Chair
___________________________________________________
STEVE KADIN, Ph.D.
Second Faculty
___________________________________________________
BETSY BATES-FREED, PsyD
Student Member
___________________________________________________
DAVID FOX, Ph.D.
External expert
Abstract
Cognitive Training has been shown to be an effective tool in enhancing
cognitive functioning. Research has also shown video game playing can improve
certain aspects of visual attention and cognitive processing speed. The purpose of
this study was to evaluate the effectiveness of both a specific computer-based
cognitive training program and non-specific video game playing in improving
cognitive functioning for individuals with age-related cognitive decline and mild
cognitive impairment. Twenty-nine older adults were recruited into the study and
randomly assigned to either the cognitive training group or video-game playing
group. Nineteen participants completed the study, engaging in either cognitive
training or video game playing for 10-15 minutes a day, 4 days per week, for
eight weeks. Multiple measures of neuropsychological functioning were
administered both before and after training. The results showed no significant
improvements in the cognitive training group, while the video game playing
group improved on measures of auditory memory and processing speed. No
significant differences were found between the two groups on any of the
dependent variables. The electronic version of this dissertation is available free at
Ohiolink ETD Center, www.ohiolink.edu/etd”.
iii
Acknowledgements
As with anything worth doing, this endeavor would not have been possible
without the support and help of a great many people.
I would like to express my deepest gratitude to my dissertation chair, Dr.
Juliet Rohde-Brown, for her guidance and support throughout this process. I
would like to thank my former advisor, Dr. Cheryll Smith for her invaluable aid in
the inception and formulation of this project. I also would like to thank Dr. Steve
Kadin and Dr. David Fox for their input and analysis of my work. Special thanks
goes to Dr. Henry V. Soper, my former second chair, and Dr. Michelle Cuevas for
their instrumental help with the statistical analysis.
I would like to thank Camilla Seippel and Dr. Katherine Burrelsman for
their help in the data collection. Thanks is also due to Dr. Annette Swain and Dr.
Gary Linker for their aid in obtaining participants.A very special thanks goes to
Dr. Betsy Bates-Freed, not only for her help in her role as student member, but
also for her friendship and motivational support. Similarly, a very special thanks
goes to (soon-to-be Dr.) Denise Jaimes-Villanueva for her friendship and support.
Finally, I would like to thank my family, my parents, Dr. Jay Fortman and
Dr. Jennifer Fortman, and my sister, Rebekka Fortman, for their unwavering
support, love, and care.
iv
TABLE OF CONTENTS
Page
ABSTRACT……………………………………………………………………...iii
ACKNOWLEDGEMENTS………………………………………………………iv
LIST OF TABLES………………………………………………………………..vi
CHAPTER
1: INTRODUCTION……………………………………………………………...1
2: LITERATURE REVIEW………………………………………………………3
Cognitive Decline…………………………………………………………3
Age-Related Cognitive Decline…………………………………...4
Neuroanatomy of Age-Related Cognitive Decline………………..5
Mild Cognitive Impairment……………………………………….6
Prevalence Rates and Risk Factors………………………………..7
Conversion to Alzheimer’s Disease and Effects…………………..8
Treatments…………………………………………………………………9
Exercise…………………………………………………………..10
Diet……………………………………………………………….11
Pharmacological Interventions…………………………………...11
Cognitive Interventions…………………………………………..13
Cognitive Stimulation……………………………………13
Cognitive Rehabilitation and Cognitive Training………..14
Computer-Based Cognitive Training…………………….15
Video Games……………………………………………..21
3. METHODS……………………………………………………………………24
Rationale for Study………………………………………………………24
Study Design and Methodology………………………………………….25
Hypotheses……………………………………………………………….26
Procedures………………………………………………………………..26
Participants……………………………………………………….26
Description of Measures…………………………………………28
Memory…………………………………………………..28
Attention/Working Memory………………………..……29
v
Processing Speed………………………………………...29
Mental Flexibility………………………………………...30
Visual-Spatial Abilities…………………………………..30
Mental Status…………………………………………….30
Test Batteries…………………………………………………….31
Research Team…………………………………………………...32
Step-wise Procedures…………………………………………….32
4. RESULTS……………………………………………………………………..35
Statistical Procedures…………………………………………………….37
5. DISCUSSION AND CONCLUSIONS……………………………………….41
Hypothesis 1……………………………………………………………...41
Memory…………………………………………………………. 41
Attention/Working Memory……………………………………..42
Processing Speed………………………………………………...42
Mental Flexibility………………………………………………...43
Visual-Spatial Abilities…………………………………………..43
Hypothesis 2……………………………………………………………...44
Previous Research………………………………………………..44
Hypothesis 3……………………………………………………………...44
Strengths…………………………………………………………………45
Limitations……………………………………………………………….45
Implications of Results and Further Study………………………………46
REFERENCES…………………………………………………………………..49
APPENDIX………………………………………………………………………60
vi
LIST OF TABLES
Table
Page
1. Demographic Characteristics…………………………………………….36
2. Multivariate Analysis by Intervention Group………………………...36-37
3. Multivariate Analysis of Combined Treatment and By Group…………..39
vii
1
Chapter I: Introduction
In the next 50 years, the proportion of older adults to the population as a
whole will more than double, increasing from 7% in 2000 to 16% in 2050 (Cohen,
2003). Researchers point to the dramatic increase in Alzheimer’s disease as the
cause of death in older adults as evidence that neurocognitive decline is the
biggest threat to successful aging in our society (Park & Reuter-Lorenz, 2009).
Alzheimer’s disease (AD), the most common etiology for dementia, is expected to
quadruple from a 2006 global prevalence rate of 26.6 million to over 100 million
individuals by 2050. In the United States, an estimated 5.3 million Americans
have AD. Every 70 seconds, someone in America develops AD. This number is
expected to decrease to every 33 seconds by 2050, and the prevalence rate in the
US is estimated to grow to between 11 and 16 million people (“2009 Alzheimer’s
disease facts and figures,” 2009). The direct (health care costs) and indirect (e.g.,
lost work productivity) costs of this dramatic shift are estimated at $148 billion
annually. The tally of direct and indirect costs fail to include an additional $94
billion in unpaid services provided annually by caregivers (“2009 Alzheimer’s
disease facts and figures,” 2009). Moreover, the numbers do not speak to the
profound emotional toll exacted on individuals, their families and friends, and
society as a whole when active lives and minds are lost to Alzheimer’s disease.
2
For reasons both economic and intangible, identifying ways to combat
neurocognitive frailty and delay or prevent the onset of cognitive decline and AD
is well worth pursuing.
3
Chapter II: Literature Review
In order to provide the framework on which the current study is based, the
following literature review is presented. The reason for the study rests on the idea
that people experience decline in cognitive functioning as they age. While most
often this decline is not pathological in nature and is simply a normal part of the
aging process, a significant number of older adults experience a
neurodegenerative process which impacts cognitive functioning to a much greater
degree than expected in normal aging and can have significant repercussions on
both those individual adults’ health and quality of life as well as present
significant financial costs in health care. As our population ages, effective
treatments which halt this cognitive decline and regain some cognitive
functioning, both in normal aging and more severe neurodegenerative processes,
is therefore, imperative. The different interventions currently used to effective
such change is discussed with particular attention to cognitive training and, more
specifically, computer-based cognitive training which is the basis for this study.
4
Cognitive Decline
Age-Related cognitive decline. Cognitive decline is generally considered
a normal part of aging. While the age of onset can vary dramatically, most adults
experience age related cognitive decline (ARCD) which can negatively affect
their quality of life (Mahncke et al., 2006). Still, debate exists as to what ARCD
actually is, or what the underlying process is. While many older adults experience
ARCD, a large number of individuals do not experience cognitive decline
(Greenwood & Parasuraman, 2010). In addition, cognitive decline does not occur
uniformly, with some research suggesting that cognitive processing speed
declines more significantly than verbal abilities and domain knowledge (Finkel,
Reynolds, McArdle, & Pedersen, 2005) and other studies concluding that memory
impairment is more common (Bjørnebekk, Westlye, Walhovd, & Fjell, 2010). In
contrast, Clay et al. (2009) posit that changes in memory and fluid intelligence are
not significant after accounting for vision and processing speed declines.
Mahncke et al. (2006) contend that, in addition to worsened sensory processing
abilities, ARCD is the result of “a self-reinforcing downward spiral of degraded
brain function” resulting from withdrawal from attention-demanding tasks and
active learning, as well as physical deterioration in the brain itself (p. 12523).
5
Much debate also exists as to the extent to which ARCD affects
functioning. While some maintain that ARCD does not compromise everyday
functioning, other research suggests even slight changes in cognitive abilities can
have a functional impact. For example, Tucker-Drob (2011) found that changes
in neurocognitive performance “were strongly correlated with individual
differences in changes in performance on… everyday tasks.” (p. 368). Mahncke
et al. (2006) wrote that ARCD “negatively impact[s] the quality of life,
independence, frequency and quality of social interaction, and engagement in
cognitively stimulating activities” (p. 12523). Moreover, these changes are
related to increased risk for nursing home placement and negative health
outcomes, including cardiovascular disease, dementia, and death(Clay et al.,
2009; Morrison-Bogorad, Cahan, & Wagster, 2007; Smith et al., 2009; Tuokko,
Garrett, McDowell, Silverberg, & Kristjansson, 2003). As Graham et al. (1997)
noted patients with ARCD “were three times more likely to be living in
institutions than were cognitively unimpaired patients” (p. 1793).
Neuroanatomy of ARCD. Neuroanatomical studies have thus far failed to
definitively identify the underlying neurological correlates to the loss of cognitive
functioning experienced in aging. Research has failed to consistently show a
relationship between volumetric cortical loss and cognitive changes (Rodriguez &
6
Raz, 2004; Van Petten, 2004; Van Petten et al., 2004) There is also no significant
neuron loss in old-age (Greenwood & Parasuraman, 2010). Synapse loss does
occur, but only after age 65 or so (Greenwood & Parasuraman, 2010). Some
research has shown cognitive aging is associated with losses in the grey and white
matter in the medial-temporal, parietal, and frontal regions of the brain (Gordon et
al., 2008); however, as Raz and Kennedy (2009) noted after an extensive review
of the literature, “the search for the neuroanatomical basis of cognitive aging has
so far yielded limited and somewhat contradictory results” (p. 59).
Mild Cognitive Impairment. It is important to distinguish between agerelated cognitive decline which, as noted, is regarded as a normal part of the aging
process, and more severe cognitive changes which may reflect a
neurodegenerative process and have more profound impacts on one’s health and
functioning. During the end of the last century, much effort was expended in
differentiating between normal, age-related cognitive decline and dementia by
defining a transitional stage between the two. While as many as 11 distinct
diagnoses were proposed, mild cognitive impairment (MCI) emerged as the most
widely accepted term. In 1999 Peterson first proposed MCI as impairment in
cognitive functioning exceeding what would be expected, but without severe
declines in everyday functioning. Peterson outlined the criteria for diagnosing the
condition which consisted of (1) subjective memory complaints, (2) the presence
7
of memory deficits on objective cognitive assessment, (3) normal general
cognitive function, (4) intact activities of daily living, and (5) the absence of
dementia (Petersen, et al., 1999). While originally the disorder focused on
memory loss as the defining feature (single-domain, amnestic MCI), MCI has
been further broken down into other subtypes, including (1) multi-domain,
amnestic MCI, (2) single-domain, non-amnestic MCI, and (3) multi-domain, nonamnestic MCI (Peterson, 2004). Typically, 1.5 standard deviations below the
mean on neuropsychological measures is considered the standard cut-off point for
establishing cognitive deficits (Petersen et al., 2001; Petersen, 2004).
Prevalence rates and risk factors. Prevalence rates for ARCD range
from 7.5% to 19.3% (Di Carlo et al., 2000; Ritchie, Artero &Touchon, 2001).
Estimates of prevalence in MCI range vary even more, from 5.3% to 34%, with
amnestic MCI accounting for approximately half the cases (Di Carlo et al., 2000;
Ganguli, 2011; Graham et al., 1997; Hänninen, Hallikainen, Tuomainen,
Vanhanen, &Soininen, 2002; Lopez, 2003a; Manly, 2005; R. C. Petersen, 2004;
Ritchie et al., 2001; Schröder et al., 1998). These widely variable estimates likely
reflect the difference in the operationalization of the MCI diagnosis through the
selection of instruments, determination of cut-off scores, inclusion/exclusion of
MCI subtypes, and other methodological disparities. Research has identified
8
several risk factors for cognitive decline, including older age, African-American
ethnicity, less than high school education, low literacy level, smoking, lack of
physical exercise, malnutrition, depression, the presence of the apolipoprotein E
e4 allele (ApoE 4), diabetes, hypertension, hyperlipidemia, and vascular disease
(Barnes, Alexopoulos, Lopez, Williamson, &Yaffe, 2006; Bordet &Deplanque,
2009; Buchman, Wilson, Leurgans, Bennett, & Boyle, 2009; Di Carlo et al., 2000;
Fiocco et al., 2009; Geda, 2010; Hong, Cheong, Oh, & Lee, 2009; Kivipelto et al.,
2001; Lopez, 2003b; Pavlik, Doody, Massman, & Chan, 2006; Tervo et al., 2004;
Wiederkehr, Laurin, Simard, Verreault, & Lindsay, 2009).
Conversion to Alzheimer’s disease and effects. The estimates of yearly
conversion rates from MCI to Alzheimer’s Disease, the most common etiology of
dementia, range from 10% to 28% (Bowen et al., 1997; Ronald C. Petersen et al.,
1999; Schmidtke & Hermeneit, 2007; Tierney et al., 1996), while in one study,
upwards of 80% of MCI patients developed dementia after 6 years (R. C. Petersen
et al., 2001). Individuals diagnosed with multi-domain amnestic MCI and
amnestic MCI are at the greatest risk for developing dementia.
In 2006, the worldwide prevalence of Alzheimer’s Disease was 26.6
million. It is estimated that by the year 2050, that number will quadruple to over
100 million. Brookmeyer, Johnson, Ziegler-Graham, and Arrighi (2007) estimate
9
that approximately 43% of AD patients will require a high level of care,
equivalent of that to a nursing home. Their research indicates that treatment
programs that delay the onset of AD by an average of two years would decrease
the worldwide prevalence rate by 22.8 million cases, saving billions of dollars.
Not only Alzheimer’s disease is associated with significant negative effects.
Graham et al. (1997) found that individuals with ARCD were three times more
likely to be living in an institutionalized setting than were cognitively intact
individuals. Additionally, older individuals’ worries about memory problems are
common and are associated with depression and anxiety (Mol et al., 2007; Reese,
Cherry, & Norris, 1999). Given the costs associated with cognitive decline, not
just financial, but personal, emotional, and societal, finding effective methods of
preventing the progression to MCI or AD or even recuperating lost cognitive
abilities in healthy adults is clearly important. As Mowszowski, Batchelor, and
Naismith (2010) point out, “with the rapidly aging population,...interventions
aimed at decreasing the social and financial costs of declining cognitive function
are irrefutably worth pursuing” (p. 537).
Treatments
Currently, research has explored several avenues for finding suitable
methods for treating or preventing cognitive decline. These have included
10
changes in behavior, including diet, exercise, and engagement in cognitively
stimulating activities, as well as pharmacological interventions. One way to
reduce the risk of cognitive decline is, of course, to eliminate the risk factors
associated with MCI and AD. For example, research has shown that cognitive
decline is less likely once cardiovascular event risks are ameliorated through the
treatment of hypertension, hypercholesterolemia, diabetes and implementation of
weight reduction and smoking cessation programs. A review of the current
treatment methods follows.
Exercise. The cognitive benefits of exercise are generally well accepted
(Colcombe& Kramer, 2003; Gordon et al., 2008; Ratey & Loehr, 2011). Research
indicates that even low levels of physical activity can improve cognitive function
(Hayashi et al., 2009). Other research suggests that high intensity aerobic exercise
is required to counteract atrophy in the medial temporal lobe and increase grey
and white matter volume (Erickson et al., 2006; Head & Bugg, 2011). In a review
of the relevant literature, Colcombe and Kramer (2003) found that “fitness
training increased performance 0.5 [standard deviations] on average [on cognitive
tests], regardless of the type of cognitive task, the training method, or
participants’ characteristics” (p. 128). For individuals already diagnosed with
Alzheimer’s disease, research suggests that physical activity does not slow the
11
rate of cognitive deterioration, but does, however, significantly reduce the
mortality risk (Scarmeas, 2011).
Diet. In addition to exercise, a healthy diet has been associated with
promoting cognitive health. Navqui et al. (2011) found that a diet high in monounsaturated fat was associated with a slower rate of cognitive decline in women.
Research has shown that adhering to the Mediterranean diet (vegetable oils, fish,
non-starchy vegetables, low glycemic index fruits, and moderate wine intake) is
associated with a number of cognitive benefits, including slowed cognitive
decline, reduced risk of conversion from MCI to AD, reduced overall risk of
developing AD, and decreased all-cause mortality in AD patients” (Sofi, Abbate,
Gensini, & Casini, 2010; Solfrizzi et al., 2011). Research has also found that the
use of ascorbic acid combined with use of metabolic precursor to uric acid, like
inosine or hypoxanthine could be helpful in maintain cognitive health (Waugh,
2008).
Pharmacological Interventions. Extensive research has been conducted
to find pharmacological interventions to treat mild cognitive impairment and
Alzheimer’s disease, with mixed results. The most common class of drug studied
for the treatment of AD has been Acetylcholinesterase inhibitors (AChEI). Based
on the finding that cholinergic deficits in the cerebral cortex and basal forebrain
12
are associated with cognitive impairment in AD, AChEI chemicals were
developed to inhibit the breakdown of acetylcholine, thus increasing both the
level and duration of action of the acetylcholine neurotransmitter (McGleenon,
Dynan, & Passmore, 1999). While research has shown modest effectiveness of
AChEI in treating moderate to severe AD, several studies have been unable to
find significant benefits in using the chemicals to treat MCI and mild AD (Allain,
Bentué-Ferrer, & Akwa, 2007). Other studies have demonstrated the potential of
these agents to slow the conversion rate from MCI to dementia, but only at the
cost of increased “adverse effects,” including vomiting and nausea, which resulted
in significantly more people dropping out of the treatment groups as compared to
placebo groups (Diniz et al., 2009; Sobów & Kłoszewska, 2007; Takeda et al.,
2006). As Sobów & Kłoszewska (2007) wrote: “Because of the questionable
efficacy: risk ratio, we believe that it is too early to recommend ChEI in MCI” (p.
11). Additionally, while the AChEI Galantamine has been shown to be efficacious
at treating MCI and mild AD, it is not a recommended form of treatment as is
been shown to increase death rates (Loy & Schneider, 2006; Sobów &
Kłoszewska, 2007). Memantine has also been studied as a potential agent to treat
AD; however, a meta-analysis reveals a lack of evidence to support its
effectiveness in treating mild AD, and scant evidence for its benefit in moderate
AD (Schneider, Dagerman, Higgins, & McShane, 2011). Moreover, no
13
pharmacological interventions exist for the treatment of non-pathological agerelated cognitive decline.
Cognitive Intervention. In addition to changes in diet, adding an exercise
program, and psychopharmacological intervention, research has shown cognitive
interventions to be effective in staving off cognitive decline and regaining
cognitive functioning already lost.
Cognitive remediation refers to “intervention strategies [used] to mediate
deterioration” in cognitive functioning (Mowszowski et al., 2010). Often the
terms “cognitive training”, “cognitive rehabilitation” and “cognitive stimulation”
are used interchangeably, masking important differences between the varied
approaches. Clare & Woods (2004) sought to remedy the situation by utilizing a
literature review to outline the differences in the three approaches. A summary of
their suggested nomenclature follows.
Cognitive Stimulation. Cognitive stimulation and reality orientation
approaches use group activities to enhance cognitive and social functioning. The
approach does not employ the structured or directed tasks associated with a
training or rehabilitation program (Clare & Woods, 2004). Instead, cognitive
stimulation may involve activities such as listening to music, baking, or engaging
in a discussion. This method has primarily been used for people who have already
14
progressed to a moderate degree of dementia, since research has shown global
cognitive stimulation to be more effective for that population than programs that
target specific cognitive functions. Spector et al. (2003) in one of the largest
randomized controlled trials, found improvements in cognition and quality of life
in patients with moderate dementia using this approach. However, as is the case
with much of the research in this area, since the comparison group was a nocontact control group, it cannot be determined whether the benefits derive mainly,
or at least partly, from the increased social interaction participation inherent in the
intervention assigned to the stimulation group.
Cognitive rehabilitation and cognitive training. Cognitive rehabilitation is
the “systematic use of instruction and structured experience to manipulate the
functioning of cognitive systems to improve the quality or quantity of cognitive
processing in a particular domain” (Robertson, 1999, p. 704). Both cognitive
rehabilitation and training involve structured activities designed to improve
cognitive and/or daily functioning. More specifically, cognitive training involves
tasks intended to stimulate mental activity in several different domains including
visual spatial skills, memory, problem solving, and attention (Sitzer, Twamley, &
Jeste, 2006). While cognitive training involves a standardized training protocol,
rehabilitation employs “individually tailor[ed] programs” (Belleville, 2008, p. 58).
Research has shown that cognitive training can yield significant improvements in
15
cognitive abilities in both MCI and normal aging populations (Belleville et al.,
2006; Belleville, 2008; Hampstead, Sathian, Moore, Nalisnick, & Stringer, 2008;
Londos et al., 2008; Valenzuela &Sachdev, 2009). However, Papp, Walsh, &
Snyder (2009), in a meta-analysis of the cognitive training literature, found only a
weighted mean effect size (Cohen’s d) of .16 across 10 randomized controlled
trials, concluding that “the existing literature is limited by a lack of consensus on
what constitutes the most effective type of cognitive training, insufficient followup times, a lack of matched active controls, and few outcome measures showing
changes in daily functioning, global cognitive skills, or progression to early AD”
(p. 50).
Computer-based cognitive training. Computer based cognitive training,
as the name suggests, utilizes a computer for the delivery of the training module.
There are several advantages to administering a cognitive training module via
computer (Gunther, Haller, Holzner, & Kryspin-Exner, 1997; Hofmann, Hock, &
Müller-Spahn, 1996). Cognitive training via a computer is likely to facilitate
motivation as it can directly measure progress and provide immediate feedback.
Additionally, it can easily customize the difficulty of the training and is “flexible
and comprehensive enough to allow systematic training of specific aspects of
cognition that may be problematic” (Günther, Schäfer, Holzner, & Kemmler,
2003, p. 201).
16
The adaptability of computer-based cognitive training becomes an
important benefit when we find that research shows variability on how people
respond to different forms of treatment based on their level of functioning.
Research (Kasten et al., 2007) indicates that individuals with MCI or dementia
benefit from cognitive interventions that focus on repetitive training tasks rather
than the explicit teaching of memory strategies. Kasten et al. (2007) hypothesizes
that this suggests that an intact hippocampal-medial temporal lobe network may
not be required to show gains from training that doesn’t rely on declarative
memory. Echkroth-Bucher & Siberski (2009) found that training via repetitive
practice exercises versus teaching training strategies showed results for mci but
not non impaired (ARCD). However, the researchers themselves suggest that
these results may in fact reflect the ceiling effects found in the measures they
used. That is to say, the Dementia Rating Scale and MMSE were likely
insufficiently sensitive enough to detect any improvement in non-impaired
individuals. In fact, ACTIVE study found that non-impaired individuals
benefitted from repetitive speed of processing training via computer training.
Moreover, while participants experience cognitive gains in all domains trained
(memory, reasoning, processing speed) they showed the greatest improvement in
the domain of processing speed, the one domain that was trained solely via
17
implicit training rather than the teaching of strategies combined with practice
exercises.
The ACTIVE study’s use of computer-training on the domain of
processing speed, like most computer-based cognitive training and rehabilitation,
is based on the principles of neural plasticity. Contrary to the long held belief that
the brain is an immutable organ, neural plasticity describes the way in which the
brain’s neural pathways and synapse change as the result of learning, changes in
behavior, or brain injury (Greenwood & Parasuraman, 2010). Typically neural
plasticity can be broken down into positive neural plasticity, which results in
increased neuronal transmission as a result of engaging in cognitive enhancing
activities, and negative plasticity, which can result when individuals withdraw
from social and cognitive experiences. Research suggests that age-related
cognitive decline is the result of negative plasticity as it is characterized by
worsened processing through the peripheral and central sensory systems (“2009
Alzheimer’s disease facts and figures,” 2009; Clay et al., 2009). Consequently,
unlike traditional cognitive training methods, which rely on the teaching of
putative strategies, computer-based cognitive training programs typically focus on
practicing perceptual speed and accuracy and implicit memory and attention
18
training (Cipriani, Bianchetti, & Trabucchi, 2006; Rozzini et al., 2007; Smith et
al., 2009).
In order to be capable in effecting positive neural plasticity, researchers
argue that the cognitive training intervention must target specific areas of
cognitive functioning. Rozzini et al. (2007) highlights the importance of training
specific areas: “Current researchers maintain that the efficacy of the rehabilitation
depends on the specificity of the training used. The aim of the particular treatment
is to modify the structure or the capability of specific cognitive functions through
the repeated administration of exercises” (p. 259). Similarly, Cipriani et al. (2006)
emphasizes the importance of cognitive training programs to incorporate intensive
practice on perceptual speed and accuracy while utilizing adaptive algorithms and
emphasizing attention and reward.
In contrast to the abundance of research on traditional cognitive training
and despite a basis in cognitive plasticity theory, only limited research exists
showing the effectiveness of computer-based cognitive training (Cipriani et al.,
2006; Hofmann, Hock, Kühler, & Müller-Spahn, 1996). While studies have
shown that computer-based cognitive training can be effective in improving
cognitive functioning in domains such as processing speed, memory, and
attention, many studies suffer from methodological concerns and limitations.
19
Most of the studies on computer-based cognitive training either failed to
include a treatment control group (Cipriani et al., 2006; Günther et al., 2003) or
utilized a simple wait-list or no-contact control group (Belleville et al., 2006;
Eckroth-Bucher &Siberski, 2009; Faucounau, Wu, Boulay, De Rotrou, &Rigaud,
2010; Rozzini et al., 2007). Some studies did include an active control group, but
often the groups did not involve utilizing a computer (Galante, Venturini,
&Fiaccadori, 2007; Schreiber, 1999; Talassi et al., 2007) or were passive in their
treatment style (e.g., watching an educational DVD on the computer) (Mahncke et
al., 2006; Smith et al., 2009). Even less well-designed research exists on the
effective use of computer-based cognitive training for healthy older adults with
ARCD.
One study that did require participants in the active control, at least in part,
to use the computer in an interactive fashion was conducted by Barnes et al.
(2009). Specifically, the study, which evaluated computer-based cognitive
training in MCI patients, utilized an active control group which involved
participants using the computer for both interactive and passive activities (i.e.,
listening to audio books, reading an online newspaper, and playing the video
game “Myst”). While they found improvement on their primary outcome
measure, this difference was nonsignificant when compared to the active control
group.
20
In addition to limited use of appropriate control groups, one of the issues
the current research on computer-based cognitive training faces is its ability to
show training effects that generalize to neuropsychological measures. Many
studies have shown that people improve in performance on the tasks on which
they are trained during the cognitive training program; however, fewer studies
have been able to show this improved performance transferring to non-trained
tasks as measured by neuropsychological instruments. This may be due to a
number of factors. Firstly, it is possible that the cognitive training program does
not sufficiently improve abilities such that they can be measured by
neuropsychological testing. Alternatively, the selection of neuropsychological
measures may limit the likelihood that any generalizable effects can be found. For
example, some studies fail to include measures that correspond to the domains on
which subjects train. In addition, some measures used in the research have been
shown to have significant ceiling effects, meaning that relatively unimpaired
individuals will not be able to improve significantly on the test given their high
pre-training level of functioning. Therefore it is imperative that the instruments
selected for such research purposes include measures that cover all the relevant
cognitive domains being trained and have demonstrated sufficient sensitivity to
reveal changes in normal functioning adults.
21
Beyond generalizability to neuropsychological measures, cognitive
training has yet to reveal consistent effects on everyday functioning. While
research has shown improvement in the activities of daily living in a dementia
population, most studies fail to find changes in everyday functioning with the
normal or mildly cognitive impaired population. This is likely due to the fact that
individuals experiencing MCI and ARCD, by definition are not significantly
impaired in their ADLs and therefore have no room for significant improvement.
Clearly the effectiveness of computer-based cognitive training still
remains largely unproven. And, even if computer-based cognitive training does
work, the question remains whether playing video games can be as effective.
Video Games. Research examining changes in cognitive functioning as a
result of video game playing dates back to 1989. Mane & Donchin (1989)
developed the “Space Fortress Game” to study complex skill acquisition.
Specifically, they endeavored “(1) to create a complex task that is representative
of real-life tasks, (2) to incorporate dimensions of difficulty that are of interest
based on existing research on skill and its acquisition, and (3) to keep the task
interesting and challenging for the subjects during extended practice” (p. 17).
Studies conducted by Gopher, Weil & Bareket (1994)and Hart & Battiste (1992)
using the space fortress game showed that skills trained during the playing of the
22
game could transfer to “real life” tasks including piloting an aircraft. As Mouck
(2010) noted:
This research showed for the first time that practice on a complex
videogame could improve performance not only on the practiced video game task,
but could also generalize to improved performance on other tasks. This
generalized learning suggests that the improvements in performance were not
only due to specialized learning of stimuli-response pairings associated with the
specific game, but were more likely caused by changes in the general cognitive
processes required by the video game, leading to the possibility of improved
performance on any other task that relies on the same cognitive processes. (p. 4).
While several studies have shown a relationship between playing action
video games and improved attention and other cognitive abilities, many of these
studies have methodological limitations. Many of the studies are of a correlational
design wherein participants are categorized as either video game users or
nonusers based on self-report of their video game playing experience. The
performances on cognitive and neuropsychological measures are then compared
between the two groups. Consequently, these studies fail to provide evidence for
causation, as it is possible that self-identified video game users play video games
precisely because of their pre-existing relative strengths in attention and
processing speed, whereas non video game users avoid playing video games due
23
to their relative weaknesses in the same cognitive domains. To rule out these
potential confounds, Green and Bavelier (2003)included as a part of their larger
study a video-game-training component. Non video game users played the action
video game Medal of Honor for one hour a day for ten consecutive days. A
control group played the Tetris video game over the same time span. The
researchers hypothesized that visual attention would improve in the action video
game group because it “require[s] that attention is distributed and/or switched
around the field [of view]”, whereas “Tetris demands focus on one object at a
time” (p. 536). Their hypothesis was confirmed as they found significant
improvements on the three dependent variables they measured and concluded
that“10 days of training on an action game is sufficient to increase the capacity of
visual attention, its spatial distribution and its temporal resolution” (p. 536).
A cognitive training study inadvertently found similar results. While
studying the effects of computer-based cognitive training, Barnes et al.(2009)
found that their active control group, which was assigned to play the video game
“Myst”, improved significantly on visual-spatial abilities and approached
significance when compared to the cognitive training group.
Similar to the dearth of quality research on computer-based cognitive
training, there is only limited methodologically robust research on the
effectiveness of video games in improving cognitive abilities.
24
Chapter III: Methods
Rationale for Study
Although it is well established that cognitive training can have positive
effects on cognitive functioning, less research exists examining the effectiveness
of computer-based cognitive training. The first hypothesis addresses this question,
stating that functioning across neuropsychological domains will improve with the
use of the computer-based cognitive training program. While some research has
shown correlations between video game playing and improved visual attention
and processing speed, little experimental evidence exists to show a causal
relationship between the two. The second hypothesis addresses this, stating that
functioning across neuropsychological domains will improve with the use of
participant-selected video games. No research in the literature has sought to
compare the effects of using a computer-based cognitive training program
specifically designed to target and train various cognitive domains with the effects
of using participant-chosen video games. As such, it is unclear whether utilizing
the cognitive training program will be more effective at improving cognitive
abilities than video games. Nevertheless, our third hypothesis addresses this,
stating that the cognitive training group will improve cognitive functioning across
domains significantly more than the video game control group.
25
Study Design and Methodology
The study utilized a single-blind controlled trial with randomized parallel
groups. The study consisted of two groups. One group utilized the computerbased cognitive training software Lumosity, while the second group played
computerized video games. The Lumosity intervention group accessed the webbased Lumosity cognitive training software’s “Basic Training” program, which
includes exercises designed to target specific cognitive domains including
memory, attention, processing speed, mental flexibility, and visual processing.
The video game control group accessed web-based video games from the website
“www.play.vg” and were free to choose the number and type of games they
played. Both intervention groups were assigned the same treatment schedule: 1015 minutes a day, 4 days a week, for 8 weeks. The experimental and control
groups both received the same type, frequency, and duration of researcher
attention, including interactions for assessments, explanation of procedures and
informed consent. All participants followed similar timelines of assessment, time
commitment, and computer exposure. Effectively, the distinguishing factor
between the two groups was that the experimental group spent their time engaged
in a comprehensive cognitive training program whereas the active control group
utilized computer video games. This active control group was selected
26
specifically to address the nonspecific factors of video game use and research
participation.
In this study, the independent variable was treatment type, either Lumosity
cognitive training or video games. The dependent variables included measures of
neuropsychological functioning in the domains of visual and verbal memory,
processing speed, attention, mental flexibility, and visuospatial abilities.
Hypotheses
1) Utilizing computer-based cognitive training (Lumosity) improves
cognitive functioning across neuropsychological domains in older adults.
2) Playing computer-based video games improves cognitive functioning
across neuropsychological domains in older adults.
3) Computer-based cognitive training is more effective at improving
cognitive functioning than playing video games across neuropsychological
domains in older adults.
Procedures
Participants. Twenty-nine participants in the Santa Barbara, Ventura, and
Los Angeles area were recruited via informational flyers and word of mouth, as
well as through brief informational presentations conducted at the Center for
Successful Aging, S+AGE (Specialized Ambulatory Geriatric Evaluation at
Sherman Oaks Hospital) and older-adult social groups. Eleven participants
27
withdrew or were excluded from the study, leaving the actual sample size as 18.
The inclusion criteria for the study limited participation to adults aged between 60
to 85 years with access to a computer and with a score greater than or equal to 23
on the Montreal Cognitive Assessment. Participants with MoCA scores lower
than 23 were excluded from the study because that level of impairment was not
the focus of this study. In addition, excluding those participants significantly
minimized the risk of including participants in the study who would be unable to
understand the risks and benefits of the experiment and, therefore, could not
ethically give informed consent. No inducement was given to participate other
than the possibility of furthering research on the benefits of cognitive training in
older adults like themselves, free access to cognitive training for the duration of
the study, and access to a summary of the results and findings of the research at
the conclusion of the study.
The only potential risk faced by participants in this study was the
possibility of emotional discomfort associated with contemplating their cognitive
status. In particular, participation in the study held the potential of revealing
cognitive deficits that participants might find distressing. Referrals were made
available for any patient who felt they required counseling to aid in the processing
of emotions that arose as a result of participation in the study. Specifically, the
contact information for licensed mental health service providers was included in
28
the informed consent form. In addition, contact information for the graduate
student research assistant and dissertation chair was provided. Both individuals
were prepared to facilitate additional community mental health referrals to any
participant who expressed discomfort associated with participation in the study.
Description of measures. Assessment tools that measure abilities in the
domains of visual and verbal memory, processing speed, attention, mental
flexibility, and visual spatial abilities were used. Given that the participant sample
included people with no measurable cognitive deficits, the assessment battery was
selected to minimize ceiling effects.
Memory. Verbal memory was measured using the Rey Auditory Verbal
Learning Test (RAVLT), while visual memory was assessed with the ReyOsterrieth Complex Figure Test (ROCF) and Modified Taylor Complex Figure
(MTCF). The RAVLT is word-list memory test in which the test administrator
read aloud a list of 15 nouns “for five consecutive trials, each trial followed by a
free-recall test” (Strauss, Sherman, & Spreen, 2006, p. 776). The RAVLT has
several different word lists. In order to minimize practice effects, a different list
of nouns was used at each evaluation. Both the ROCF and MTCF involved the
participant copying a complex figure and then drawing it from memory (Strauss et
al., 2006). In order to minimize practice effects, half the participants took the
29
ROCF test during the first assessment and the MTCF during the second
assessment, and the other half of the participants took them in the reverse order.
Attention/Working memory. Attention and working memory was
primarily measured using the Wechsler Adult Intelligence Scale – Fourth Edition
(WAIS-IV) Digit Span subtest. This test is comprised of three separate tasks
(Digit Span Forward, Digit Span Backward, and Digit Span Sequencing) which
required individuals to listen to a string of digits and repeat back the numbers in
the same order, reverse order, or ascending numerical order,
respectively(Wechsler, 2008).
Processing Speed. Two types of processing speed, psychomotor speed and
verbal fluency, were measured. Psychomotor speed was assessed by both Trail
Making Test part A and the WAIS-IV Coding subtest. The Trail Making Test part
A, constructed in 1938 and adapted by Reitan in 1955, required the participant to
“connect, by making pencil lines, 25 encircled numbers randomly arranged on a
page in proper order”(Strauss et al., 2006, p. 655). The WAIS-IV Coding subtest
is a “core Processing Speed subtest” in which the “examinee copie[d] symbols
that [were] paired with numbers within a specified time limit” (Wechsler, 2008, p.
16). Verbal fluency was measured by both phonemic and semantic fluency tasks.
For the phonemic fluency test participants were given three trials of one minute
30
each in which to generate as many words as possible that began with a specific
letter. Semantic fluency asked the participant to generate in one minute as many
words as possible within a specific category (e.g., animals or vegetables)(Strauss
et al., 2006).
Mental Flexibility. Mental flexibility was measured by both the Golden
Stroop task and Trail Making Test B (TMT B). The Golden Stroop test required
participants to “suppress a habitual response in favor of a less familiar one”
(Strauss et al., 2006, p.477). More specifically, in the target task, participants
were shown cards with rows of color names (blue, green, red) printed in colored
ink different than the word itself (e.g., the word “blue” would be printed in red or
green ink) and asked to name the color of the ink rather than read the word. TMT
B required the participant to connect, as quickly as possible, “25 encircled
numbers and letters in alternating order” using a pencil (Strauss et al., 2006, p.
655).
Visualspatial Abilities.Visualspatial abilities were measured by the copy
portion of the Rey-Osterrieth Complex Figure Test (ROCF) and Modified Taylor
Complex Figure (MTCF).
Mental Status. The Montreal Cognitive Assessment (MoCA), a brief
mental status exam, was used as a screening tool to rule out participants who
31
exhibited signs of dementia or serious cognitive impairment. The test measures
abilities in several cognitive domains, as outlined by Nasreddine et al.(2005):
The short-term memory recall task (5 points) involves two learning trials
of five nouns and delayed recall after approximately 5 minutes.
Visuospatial abilities are assessed using a clock-drawing task (3 points)
and a three-dimensional cube copy (1 point). Multiple aspects of executive
functions are assessed using an alternation task adapted from the Trail
Making B task (1 point), a phonemic fluency task (1 point), and a twoitem verbal abstraction task (2points). Attention, concentration, and
working memory are evaluated using a sustained attention task (target
detection using tapping; 1 point), a serial subtraction task (3 points), and
digits forward and backward (1 point each). Language is assessed using a
three-item confrontation naming task with low-familiarity animals (lion,
camel, rhinoceros; 3points), repetition of two syntactically complex
sentences (2 points), and the aforementioned fluency task. Finally,
orientation to time and place is evaluated (6 points).
Test Batteries. Two Batteries (Battery A and Battery B) were developed
using alternate forms of some tests in order to minimize practice effects. Battery
A consisted of RAVLT List 1, Phonemic Fluency FAS, Semantic fluency
Animals, the Digit Span subtest, the Coding subtest, TMT A and B, the Stroop,
32
and the Rey Complex Figure. Battery B consisted of RAVLT List 2, Phonemic
fluency CFL, Semantic Fluency Vegetables, the Digit Span subtest, the Coding
subtest, TMT A and B, the Stroop, and the Modified Taylor Complex Figure. The
first 12 participants recruited were administered Battery A at time 1. Participants
numbered 13 through 26 received Battery B at time 1. The final 3 participants
completed Battery A at time 1. All participants who completed testing at time 2
received the alternate battery at that time.
Research Team. The research team included two supervising licensed
psychologists, one post-doctoral fellow, and two graduate students (one of whom
was the primary investigator). The post-doctoral fellow and two graduate students
conducted all the neuropsychological assessments.
Step-Wise Procedures.
Step 1- Prior to meeting with the participant, in order to prepare the correct
paperwork, the participant was assigned to one of the two experimental groups via
the toss of a coin, with results as follows: “heads” = cognitive training group,
“tails” = video game group.
Step 2- Upon meeting with the participant, informed consent was explained,
including the risks and benefits of the study and how the results would be kept
confidential. The only identifying piece of information on each questionnaire and
33
test result was a code number, linked to the participant’s name only through their
signed informed consent form, which was kept in a secured location.
Step 3- Once the participant signed the consent form, the Montreal Cognitive
Assessment was administered. All participants met inclusion criteria of MoCA
score >23 and accordingly no participants were excluded from the study at this
point.
Step 4- The neuropsychological instruments (Battery A or Battery B)were
administered to the participant in a quiet room free from distractions.
Step 5 – The participant was given the printed instructions specific to their group
assignment (see appendix), along with two record sheets. Participants were shown
how to record the date and time of their sessions, along with the specific activities
or games they utilized.
Step 6- Participants engaged in the 8-week intervention specific to their
experimental group during which time support was available via telephone or
email. Participants accessed the cognitive training software or video game
software from their personal computers. Two participants contacted the
researchers via email with questions about “logging in” to the cognitive training
website. One participant requested instruction in using the video games. In-person
instruction was provided to this participant.
34
Step 5- The participant underwent a second battery of neuropsychological tests
following completion of the intervention period.
Participants who completed fewer than eight sessions in the first four
weeks or skipped eight consecutive sessions thereafter were discontinued from the
study. In addition, participants were free to withdraw consent at any time during
the study. As noted, 11 participants were excluded from the final analysis, as 5
explicitly withdrew from the study and 5 failed to complete a sufficient number of
training sessions to be included in the study. The remaining individual was not
included in the final analysis as he/she was not able to complete all trials of the
Stroop test due to color-blindness.
35
Chapter IV: Results
Data was analyzed using the SPSS version 20.0 for Windows. All
procedures were approved by Antioch University Santa Barbara’s Institutional
Review Board for Human Use. Data was stripped of identifying information to
protect the privacy of study participants.
Descriptive statistics for all cognitive measures are displayed in Table 1.
Sixteen of the 29 participants were assigned to the cognitive training group and
the remaining 13 were assigned to the video game group. Of the twenty-nine
individuals who participated in the study, 10 either withdrew from the study or
did not complete enough training sessions to be included in the statistical analysis,
while one was excluded for not completing all measures administered in the test
battery, making a final sample of 18 individuals. The sample of 18 individuals
had a mean age of 70.33±6.30 years, four reported some college education, five
were college graduates, two reported some post-graduate education and 7 reported
post-graduate degrees. There were 16 females and 2 males. Four individuals
scored below normal on the MoCa (<26) and the mean MoCA score was
27.00±2.08. These characteristics are consistent with the full sample of 29
individuals. There were no significant differences between the final sample of 18
individuals and the 11 individuals excluded from the analysis. Of the eleven
36
Table 1
Demographic Characteristics
N=18
Gender
Female
Male
Education
Some college
College graduate
Some graduate school
Graduate degree
Occupation
Executive/Professional
Skilled/Technical
Frequency
Percent
16
2
88.9
11.1
4
3
2
7
22.2
16.7
11.1
38.9
13
3
72.2
16.7
Table 2
Multivariate Analysis by Intervention Group
Treatment Group
Measure
Cognitive
Training
(N=9)
RAVLT Total Score
RAVLT ImmRecalla
RAVLT Delay Recall
CFT Copy
CFT ImmRecallb
CFT Delay Recall
Phonemic Fluency
Semantic Fluency
Digit-Span Forward
Digit-Span Backward
Digit-Span Sequence
Digit-Span Total
Coding
Trail Making Test A
Trail Making Test B
Stroop CW Interc
Time 1
Time 2
F value
p value
105.11 (14.56)
104.44(17.76)
109.44(17.76)
99.33(8.59)
107.00(25.71)
109.22(27.51)
107.67(14.36)
90.33(19.63)
98.89(16.35)
103.33(10.90)
105(11.46)
101.11(13.64)
112.22(11.76)
93.11(15.54)
98.44(11.01)
102.22(8.80)
118.22(14.41)
116.11(12.69)
116.11(14.53)
105.56(8.83)
110.89(28.05)
107.44(27.44)
110.67(9.22)
95.22(11.19)
96.67(8.67)
107.78(7.55)
103.89(7.82)
103.89(9.28)
117.22(11.21)
100.33(11.15)
102.00(7.81)
107.22(8.66)
0.26
0.96
0.22
3.13
1.22
1.99
0.59
0.17
3.36
3.53
0.52
4.65
0.40
0.67
0.74
0.17
0.63
0.37
0.66
0.13
0.31
0.21
0.48
0.69
0.12
0.11
0.50
0.07
0.55
0.44
0.42
0.70
37
Table 2 Continued
Video Games (N=9)
RAVLT Total Score
RAVLT ImmRecalla
RAVLT Delay Recall
CFT Copy
CFT ImmRecallb
CFT Delay Recall
Phonemic Fluency
Semantic Fluency
Digit-Span Forward
Digit-Span Backward
Digit-Span Sequence
Digit-Span Total
Coding
Trail Making Test A
Trail Making Test B
Stroop CW Interc
98.0 (8.43)
105.55(12.61)
105.00(12.99)
101.78(6.28)
114.67(10.36)
119.67(6.91)
106.00(12.35)
98.78(20.12)
98.33(11.99)
102.22(7.12)
102.78(13.02)
101.11(9.93)
109.44(7.27)
90.89(15.85)
92.89(12.61)
108.00(9.35)
114.0 (14.05)
111.67(18.20)
110.00(14.58)
100.44(14.01)
122.44(14.05)
122.00(14.05)
105.78(20.11)
97.33(16.76)
96.11(7.82)
104.44(16.67)
105.00(8.29)
102.78(10.03)
114.44(10.74)
104.56(10.93)
99.56(11.81)
110.11(10.50)
14.76
9.86
16.55
0.04
0.02
0.18
0.22
1.97
1.50
0.70
0.16
0.20
8.40
2.88
3.24
1.42
* p<0.05 ** p<0.01
a=RAVLT Immediate Recall b=Complex Figure Test Immediate Recall c=Stroop Color-Word Interference
individuals excluded from the analysis, seven were in the cognitive training group
and four in the video game group. The 11 participants excluded from the sample
had a mean age of 65.50+4.04, one reported some college, one was a college
graduate, and were five reported earning post-graduate degrees. Education
information was unavailable for the remaining four.
Statistical Procedures
Hypothesis 1 tested whether utilizing the structured cognitive training
program Lumosity improved the participants’ performance on the
neuropsychological measures administered. To test whether these changes in
performance were significant, a multivariate analysis with repeated measures was
<0.01**
0.02*
<0.01**
0.84
0.89
0.70
0.65
0.21
0.27
0.44
0.70
0.67
0.03*
0.14
0.12
0.28
38
conducted. The results are presented in Table 2. No significant differences were
found on any of the dependent variables.
Hypothesis 2 tested whether utilizing freely available video games in an
unstructured, participant-selected manner improved the participants’ performance
on neuropsychological measures. To test whether these changes in performance
were significant, a multivariate analysis with repeated measures was conducted.
The results are presented in Table 2. Significant improvement in scores were
noted on memory measures including the RAVLT total recall score (F(1,16) =
14.76, p< .01), RAVLT Immediate Recall (F(1,16) = 9.86, p = .02), RAVLT
Delayed Recall (F(1,16) = 16.55, p <.01), and WAIS-IV Coding subtest (F(1,16)
= 8.40, p = .03). No significant differences were found on the remaining
dependent variables.
Hypothesis 3 tested whether using the cognitive training program
improved participants’ performance on neuropsychological measures more so
than using video games. First, to test whether the intervention in general (both
cognitive training and video games) significantly improved performance on
neuropsychological measures a multivariate analysis with repeated measures was
conducted. Overall, no significant improvement was found, while examining
individual measures revealed improvements in RAVLT Total Score (F(1,16) =
29.38, p <.001) Immediate Recall (F(1,16)=8.46, p=0.01), Coding
39
(F(1,16)=14.40, p =0.002), Trail Making Test Part A (F(1,16)=11.05, p=0.004),
and Trail Making Test Part B (F(1,16)=4.71, p=0.05). The complete results are
presented in Table 3. Analysis did not yield a significant interaction between
intervention nor were significant interaction effects found on any of the dependent
variables. The results are presented in Table 3.
Table 3
Multivariate Analysis of Combined Treatment and By Group Comparison
Measure
Mean
N=18
RAVLT Total Score
a
RAVLT ImmRecall
RAVLT Delay Recall
CFT Copy
Time 1
Time 2
F
Sig.
101.56(12.10)
116.11(13.98)
29.38
105.00(14.95)
107.22(15.27)
100.56(7.40)
b
CFT ImmRecall
CFT Delay Recall
Phonemic Fluency
Semantic Fluency
Digit-Span Forward
Digit-Span Backward
Digit-Span Sequence
Digit-Span Total
Coding
110.83(19.41)
114.44(20.20)
106.83(12.02)
94.57(19.77)
98.61(13.91)
102.78(8.95)
103.90(11.95)
101.11(11.58)
110.83(9.56)
Trail Making Test A
Trail Making Test B
c
Stroop CW Inter
Combined Treatment
92.00(15.27)
95.67(11.84)
105.11(9.29)
113.89(15.40)
113.06(14.47)
103.00(11.71)
116.67(22.33)
114.72(22.43)
108.22(15.39)
96.28(13.87)
96.39(8.00)
106.11(12.67)
104.44(7.84)
103.33(9.40)
115.83(10.74)
102.44(10.93)
100.78(9.80)
108.67(9.45)
8.46
2.31
.81
1.08
.002
.19
.34
.86
1.85
.04
1.77
14.40
11.05
4.71
1.64
* p<.05. ** p<.01. *** p<.001.
a=RAVLT Immediate Recall b=Complex Figure Test Immediate Recall c=Stroop Color-Word Interference
By Group
Sig.
<.001***
F
.29
<.01**
.83
.38
.15
.05
.83
.38
1.92
.19
.32
.12
.73
.96
.13
.73
.67
.25
.62
.57
1.13
.30
.37
0
1
.19
.21
.66
.84
.37
.55
.20
.11
.74
.002**
0
1
.004**
1.05
.32
.05*
.44
.52
.22
.27
.61
.60
40
Chapter V: Discussion and Conclusions
The goal of this study was to evaluate the effectiveness of a computerbased cognitive training program compared to the use of non-specific video game
playing. We utilized the cognitive training program Lumosity for the
experimental treatment group since it contained a specific training program
designed to target the cognitive domains of attention, memory, visual spatial
abilities and mental flexibility. This condition was compared to our active control
group, which consisted of participant-selected video games from the “play.vg”
web site.
Hypothesis 1
Hypothesis 1 stated that utilizing the cognitive training program would
improve cognitive functioning across neuropsychological domains. No support
was found for this hypothesis as participants did not significantly improve on any
of the measures.
Memory. The results are similar to those found by Eckroth-Bucker &
Siberski (2009) and Cipriani et al. (2006) who failed to find improvements in
auditory memory, specifically story memory, following computer-based training
in unimpaired participants. Conversely, Mahncke et al. (2006) found
41
improvements in auditory memory in cognitively healthy participants who used a
computer-based training program which “intensively exercise[d] aural language
reception accuracy” and required individuals to “perform increasingly more
difficult stimulus recognition, discrimination, sequencing, and memory tasks
under conditions of close attentional control, high reward, and novelty” (p.12524).
Similarly, Smith et al. (2009) and Belleville et al. (2006) found improved rote
verbal memory and list learning ability in cognitively healthy older adults. Other
research has found similar improvements in auditory memory with mild to
moderately impaired individuals (Belleville et al., 2006; Günther et al., 2003;
Rozzini et al., 2007).
Attention/Working memory. The current study’s lack of significant
improvement on a measure of attention/working memory (WAIS-IV Digit Span
Backwards) is similar to Barnes et al. (2009), Belleville et al. (2006), and
Eckroth-Bucker &Siberski (2009) who failed to find significant improvements in
this domain in either cognitively healthy or mildly impaired individuals..
Conversely, Smith et al. (2009) found significant improvements on the same
attention task, and Mahncke (2006) found improved digit span recall even after a
3 month no contact follow-up.
Processing Speed. The current study failed to find significant
improvement in cognitive processing speed for the cognitive training group.
42
While this result is similar to research conducted by Barnes et al. (2009) and
Cipriani et al. (2006), it is at odds with the large multi-site ACTIVE (Ball et al.,
2002)study which utilized computers for “speed-of-processing” training. In their
sample of healthy older adults significant improvement was found on a measure
of cognitive processing speed. However, it should be noted that the
neuropsychological instrument used to measure cognitive processing speed (The
Useful Field of View test) is itself administered on the computer thus limiting the
generalizability of their findings. In fact, on their measures of “everyday speed”
they failed to find significant improvement following cognitive training. The
current study’s failure to find significant improvements in verbal fluency, both
semantic and phonemic fluency, is consistent with previous research (Barnes et
al., 2009; Cipriani et al., 2006; Rozzini et al., 2007).
Mental Flexibility. Similar to the current study, previous research has
also failed to show significant improvements in performance on the Trail Making
Test (Barnes et al., 2009; Cipriani et al., 2006; Günther et al., 2003).
Visual-Spatial Abilities. Similar to the results of Barnes et al. (2009) and
Rozziniet al. (2007), cognitive training participants in this study failed improve
significantly on measures of visuo-spatial functioning. Conversely, mildly
impaired individuals in a study conducted by Talassi et al. (2007) showed
43
significant improvement in cognitive functioning only in the domains of visual
construction.
Hypothesis 2
Hypothesis 2 stated that utilizing computer-based video games would
improve cognitive functioning across neuropsychological domains. Partial support
was found for this hypothesis as participants improved significantly on 4 of the 16
dependent variables measured, including in the domains of auditory memory,
specifically rote verbal memory and list learning ability (RAVLT Total Score,
Immediate Recall, &Delay Recall) and processing speed (WAIS-IV Coding). No
significant improvement was found in the remaining domains.
Previous Research. Comparison to previous research is limited due to the
lack of randomized clinical trials examining the effects of computer video games
on cognitive abilities in older adults. Similar to the current study, previous
research has shown increased cognitive processing speed to be associated with
video game use (Green & Bavelier, 2003). Unlike the current study, prior research
has shown utilizing video games can improve visual attention (Green 2003) and
immediate visual memory (Green & Bavelier, 2003). A unique finding of the
current study was the significant improvement on verbal memory tasks found in
the video game group.
44
Hypothesis 3
Hypothesis 3 stated that utilizing computer-based cognitive training improves
cognitive functioning more so than does playing video games. First it must be
determined whether the intervention in general (both cognitive training and video
games) significantly improved performance on neuropsychological measures.
Overall, no significant improvement was found, whereas examining individual
measures revealed improvements in RAVLT Total Score and Immediate Recall,
Coding, and Trail Making Test Part A& B. On all the neuropsychological
measures administered, no significant differences were found between the
cognitive training group and video game group. Consequently, no evidence was
found to support hypothesis 3.
Strengths
The current study contains several strengths. The current study utilized a
blinded randomized trial with pre-test and post-test measures. Unlike many
previous studies, an active control group which engaged in interactive software on
a computer was utilized. The study also measured functioning in all the cognitive
domains on which training occurred and included measures sensitive enough to
show improvements even in cognitive healthy adults.
45
Limitations
Several limitations of the current study prevent further generalization of
the results. This study had a small number of participants (n=29) initially, and a
35% dropout rate, resulting in a statistical sample of only 19 individuals. The
study was a single, not double-blind study. While participants were blind to their
group participation, the study assessors were aware of the participants’ group
membership. The training schedule was only for 8 weeks. Perhaps a longer
treatment schedule would have resulted in more significant results. The
participant sample on the whole was rather homogenous group. Only 3
participants were male. Participants were also highly educated; 13 of the 19
participants had college degrees or above, including 8 participants having earned
graduate degrees. Fourteen participants identified as having worked as a
professional or executive, three reported being skilled workers, and two declined
to answer. The significant improvements found in both the experimental and
active control group could be interpreted as practice effects since only a
comparison with a wait-list or no-contact control group could definitively rule out
this possibility.
Implications of results and further study
The current study suggests that utilizing a computer for interactive
software, either video games or cognitive training, can positively affect cognitive
46
functioning, specifically auditory memory, processing speed, visual attention, and
mental flexibility. Moreover, the results indicate that computer based cognitive
training does not provide significantly greater improvement than non-specific
video game playing, suggesting that interactively using a computer for cognitively
engaging exercises is adequate for producing some positive cognitive effects. As
noted above, cognitive decline can lead to higher rates of depression and anxiety;
consequently, computer-based interactive activities represent another avenue of
intervention for clinicians who treat older adults experiencing cognitive decline
and the associated negative emotional consequences.
As noted above, the current study suffers from several limitations which
future research should seek to remedy. Further research would benefit from the
inclusion of a no-contact control group to account for the possibility of practice
effects. As previous research has shown passive use of a computer to be
ineffective in promoting cognitive growth, it would be interesting to utilize
various treatment groups with different levels and types of computer interaction in
order to parse out what specific aspects of interactivity are essential in effecting
cognitive change. Utilization of a double-blind study design would also add to the
methodological robustness of the study. Follow-up testing after 6 months to one
year after the conclusion of cognitive training would provide evidence for or
against the persistence of the cognitive improvements. A larger, more diverse
47
sample would enable for greater generalization of the results. Specifically, further
research would benefit from the inclusion of more male participants and
individuals with a wider range of age, cognitive functioning, and educational and
employment background. While not the focus of the current study, the inclusion
of a measure of independent activities of daily living as well as a
depression/mood measure would allow future research to speak to the
effectiveness of computer-based cognitive intervention in the broader emotional
and day-to-day functioning of older adults.
48
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Appendix
A
#__________
d Consent F
Form
Informed
Antioch
A
Univ
versity is com
mmitted to th
he ethical prrotection of pparticipants in
reesearch. Thiis form will provide you
u with inform
mation aboutt the study soo that you
caan decide wh
hether you wish
w to particcipate. Partiicipation in tthis study is
voluntary and
d anonymouss. Your answ
wers and resuults will be iidentified onnly by a
co
ode number,, not by yourr name.
The
T study is about
a
age rellated cognitiive decline. T
The study m
may require a
siignificant tim
me commitm
ment. Particip
pants will unndergo two nneuropsychoological
ev
valuations which
w
will require 45 min
nutes to 1 hoour to compllete. The evaaluations
will
w involve being
b
asked to
t complete certain taskks designed too measure yyour
memory,
m
atten
ntion, and otther cognitiv
ve abilities. Y
You can receeive a summ
mary of
you evaluatio
on results and
d, if you wissh, can partaake in a feedbback sessionn to go
ov
ver your resu
ults in more detail. Partiicipants will need to com
mmit to usingg a
co
omputer for 10-15 minu
utes a day, 4 days a weekk, for 8 weekks. Participannts will
use the compu
uter to engag
ge in activitiies requiringg the use of ttheir memoryy,
atttention, and
d other cogniitive abilitiess.
Iff you decide to participatte, your resu
ults may helpp researcherss understandd the
efffectiveness of computerr based cogn
nitive activitiies in improving thinkinng
ab
bilities. Whiile it is unlik
kely, the posssibility existts that underggoing the
neuropsychollogical evalu
uations and/o
or engaging in the cognittive activitiees may be
psetting, in that
t participaants’ cognitiive weaknes ses may be rrevealed. Bee assured
up
th
hat if this hap
ppens, you may
m contact the licensedd psychologists listed bellow for
co
ounseling an
nd support:
60
Rebecca Goodman, P.hD.
22 W. Micheltorena St, Suite B
Santa Barbara CA 93101
Phone: (805) 563-2644
Annette Swain
15928 Ventura Blvd, Suite 231
Encino CA 91436
(818) 385-0913
You may also contact the study investigators with your concerns, and steps will
be taken to insure that you receive a list of local resources that can also provide
counseling and support to you.
If you have any further questions concerning this
study please feel free to contact research assistant
Camilla Seippel, M.A., or Juliet Rohde-Brown,
PhD., at Antioch University Santa Barbara, 801
Garden Street, Santa Barbara, California, 93101,
(805) 962-8179. If you agree to the terms of this
agreement, and wish to include your answers to the
questionnaire in this study, please sign on the space
below that you understand your rights and agree to
participate in this study.
Your participation is invited, yet strictly voluntary. All information will be kept
confidential and your name will not be associated with any research findings.
________________________________
Signature of Participant
James Fortman, M.A., Investigator
Antioch University Santa Barbara
(Print name)
Juliet Rohde-Brown, PhD., supervisor
Antioch University Santa Barbara
61
Demographic Questionnaire
#
Age:
Sex (please circle one): Male Female
Educational level obtained (High school, graduated high school, some college,
graduated from college, some post-graduate work, post-graduate degree (circle
highest degree obtained).
Circle category that best describes your occupation
Executive/managerial/professional
Skilled technical/clerical/service
Labor/manufacturing
Have you ever had a brain injury, stroke, or brain tumor?
Have you ever had a concussion?
Have you ever had general anesthetic?
Have you been diagnosed with diabetes?
Do you have circulatory problems/heart issues?
Please list medications you currently take
What physical activities do you engage in?
How often? (rarely, monthly, weekly, daily)
62
How often do you use a computer? (please circle one): (never, rarely, monthly,
weekly, daily)
What do you use the computer for? (circle as many that apply):
Email
Research
Social networking
Instant messaging
Games
Word processing documents
Other:
Do you engage in any of the following activities? (circle as many that apply):
Crosswords
Sudoku
Board games
Art
Reading
Watching television/movies
Continuing education
Email address:
63
LUMOSITY INSTRUCTIONS FORM
ID #
USE THE LUMOSITY TRAINING:
- COMPLETE THE DAY’S TRAINING PROGRAM
- 4 TIMES PER WEEK
- FOR 8 WEEKS
TRY TO ADHERE TO A TRAINING SCHEDULE AS BEST YOU CAN, BUT
IF YOU MISS A FEW SESSIONS, DON’T GIVE UP! JUST CONTINUE
TRAINING AS USUAL.
HOW TO START:
1. Open web browser
2. Type “lumosity.com” in the address bar
3. Click on “Start Training”
4. If it prompts you for your login and password, use the ones provided
below.
5. Record date and start time on provided record sheet
6. Complete the day’s training
7. Record end time on record sheet.
8. Record the names of the games you played
9. DO NOT DO ANY EXTRA TRAINING EXERCISES OR TAKE
ASSESSMENTS
64
LOGIN:
PASSWORD:
VIDEO GAME INSTRUCTIONS FORM
ID:
PLAY THE PROVIDED GAMES:
- FOR 10-15 MINUTES
- 4 TIMES PER WEEK
- FOR 8 WEEKS
TRY TO ADHERE TO A TRAINING SCHEDULE AS BEST YOU CAN, BUT
IF YOU MISS A FEW SESSIONS, DON’T GIVE UP! JUST CONTINUE
TRAINING AS USUAL.
HOW TO START:
1. Open web browser.
2. Type http://www.play.vg/ into the web browser address bar.
3. Select from the available games
4. Record date and start time on provided record sheet
5. Spend 10-15 minutes playing.
6. Record end time on record sheet.
7. Record the names of the games you played
65
ACTIVITIES LOG
ID # ________
DATE
TIME
START
TIME END
GAMES PLAYED
66
Form B
Insuring Informed Consent of Participants in Research:
Questions to be answered by AUSB Researchers
The following questions are included in the research proposal.
1. Are your proposed participants capable of giving informed consent? Are
the persons in your research population in a free-choice situation?…or
are they constrained by age or other factors that limit their capacity to
choose? For example, are they adults, or students who might be beholden
to the institution in which they are enrolled, or prisoners, or children, or
mentally or emotionally disabled? How will they be recruited? Does the
inducement to participate significantly reduce their ability to choose
freely or not to participate?
The participants in my study, adults aged 60-85 years of age without
dementia, are capable of giving informed consent. The decision to participate
in the study is completely voluntary, as will be explained in the accompanying
documents. The only identifying information on the demographic
questionnaire and test results will be a code number. A single master list
associating participant name and code number will be kept under
lock.Participants will be recruited via informational flyers and brief
informational presentations conducted at local retirement communities,
assisted living homes, and social groups and through word of mouth. There
will be no inducement to participate other than the possibility of furthering
research on the benefits of cognitive training in older adults like themselves,
the possibility of free access to cognitive training for the duration of the study.
2. How are your participants to be involved in the study?
Potential participants will fill out an informed consent agreement,
demographic questionnaire and undergo a mini-mental status exam. If selected
for the study, participants will be evaluated on two occasions using the
psychological and neuropsychological test instruments discussed above.
Depending on which group they are assigned to, participants will either
partake in aninternet-based cognitive training program or play free internetbased video games for 10-15 minutes a day, 4 days a week, for 8 weeks.
3. What are the potential risks – physical, psychological, social, legal, or
other? If you feel your participants will experience “no known risks” of
67
any kind, indicate why you believe this to be so. If your methods do
create potential risks, say why other methods you have considered were
rejected in favor of the method chosen.
The only potential risk faced by participants in this study might be emotional
discomfort associated with contemplating their cognitive status. In particular,
participation in the study may reveal cognitive deficits which the participant
may find distressing.Referrals will be available for any patient who feels they
might require counseling to aid in the processing of emotions that might arise
as a result of participation in the study. Specifically, the contact information
for licensed mental health service providers will be included in the informed
consent form. In addition, contact information for thegraduate student
research assistant and dissertation chair will be provided. Both individuals
will be prepared to facilitate additional community mental health referrals to
any participant who expresses discomfort associated with participation in the
study.
4. What procedures, including procedures to safeguard confidentiality, are
you using to protect against or minimize potential risks, and how will you
assess the effectiveness of those procedures?
The only identifying piece of information on each questionnaire will be a code
number, which will linked to a participants name only through a single master
list which will be kept in a locked cabinet.Upon completion of data collection,
these records will be kept in a secured location for a period of 5 years, at
which time they will be shredded.
5. Have you obtained (or will you obtain) consent from your participants in
writing? (Attach a copy of the form.)
Each participant will be asked to review and sign an informed consent
document at the outset of the initial interview
6. What are the benefits to society, and to your participants, that will accrue
from your investigation?
Age related cognitive decline affects the quality of life of millions of people
and as such, effective treatments for ARCD will benefit a large portion of the
population. This study will contribute to the body of research on determining
the effectiveness of cognitive training as treatment for ARCD. In addition,
participants in the study will receive 2 neuropsychological evaluations free of
charge, a service that typically costs over $1000, and will be provided with a
hard copy of their results.
68
7. Do you judge that the benefits justify the risks in your proposed
research? Indicate why.
I believe that the risks associated with participation in this survey are minimal
and clearly are outweighed by potential benefits to society associated with
enhancing understanding of the effectiveness of cognitive training.
Both the student and her Dissertation Chair must sign this form and submit
it before any research begins. Signatures indicate that, after considering the
questions above, both student and faculty person believe that the conditions
necessary for informed consent have been satisfied.
Date:___________________________
Signed:_____________________________
Student
Date:___________________________
Signed:_____________________________
Dissertation Chair
When completed, this form should be included in the proposal and the final paper.
69